Result Analysis: Along with the designed baselines, we incorporate our iterative design on top of three popular state-of-the-art (SOTA) feed-forward text recognition frameworks – a) Shi  [1]}, b) Show, Attend and Read [2]} c) SCATTER [3]}. Show, Attend and Read [2]} extends [1]} by including 2D attention and SCATTER [3]} couples multiple BLSTM layers for richer context modelling. We follow similar training protocol in [1]}, [2]}, [3]} respectively. Nevertheless, ours is a meta-framework and could be added on top of most SOTA frameworks. Table REF  (highest scores are in red) depicts unconstrained word recognition accuracy (WRA) on unseen words (DS setup). [i] Comparison with Baselines: Seq-SCN performs inferior to our method as the rich visual feature is not harnessed while refining the prediction. At times it fails to copy an already accurate prediction, leading to a lower accuracy in certain datasets. Deterministic-Feedback being an iterative framework, performs better than other baselines at \(t=2\) , however, it lags behind our design since any uncertainty handling potential is absent. [ii] Significant improvement under DS setup: From Table REF , improvement due to our iterative pipeline is quite evident in the DS scenario over three SOTA baselines [1]}, [2]}, [3]} . Similarly, in handwritten dataset (Table REF ), the performance drop in DS setting is way more severe than its STR counterpart. This signifies that HTR poses a greater challenge due to its free-flow nature of writing. Improvement against Shi [1]} reaches to 10.5% and 10.8%, without lexicon information (unconstrained) in HTR dataset of IAM and RIMES respectively, while for STR dataset of IC15 it secures a 6.9% rise in DS setup. [iii] Additional Observations: Improvement at \(t\) =2 w.r.t \(t\) =0 is shown as relative gain where our method outperforms fairly in CS setup, and largely in DS setup (Table REF ). In fact optimal WRA is seen at iteration \(t=2\) , which then diminishes. Contrary to a possible impression, that our feedback module might remember the erroneous pairings from \(\mathcal {G}\) , the improvement on “unseen" words empirically validates against it.
Figure REF  shows qualitative results.
